I recently joined the Qwen Cloud Hackathon and decided to tackle a problem that many engineering teams face: cloud incidents are repetitive, expensive, and often require engineers to investigate the same issues over and over again.
My project is called Autonomous Cloud Operations Team.
Instead of a single AI assistant, the system is designed as a team of specialized agents that collaborate to investigate incidents, identify root causes, propose remediation steps, and learn from previous outages.
The initial agent architecture includes:
Monitoring Agent
Incident Agent
Root Cause Agent
Remediation Agent
Memory Agent
The most interesting component is the Memory Agent.
Most systems store logs and incident records, but they rarely learn from them. My goal is to create an operational memory layer that can recognize recurring incident patterns and surface lessons learned from previous incidents.
For example:
A latency spike occurring today may resemble an outage that happened six weeks ago. Instead of starting from scratch, the system can retrieve the previous root cause analysis and remediation steps.
Since this is a cloud-focused hackathon, I am also exploring Alibaba Cloud-native integrations and designing the system specifically around cloud operations workflows.
Over the next few weeks I'll be sharing progress updates, architecture decisions, challenges, and lessons learned as I continue building.
Stay tuned.
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